In the neural networks section we tell people to do a 4 way cross validation and once those are complete to retrain on the entire dataset. Is this really the right way to do things? In other neural net frameworks (e.g. keras) you can do multiple iterations of training, so that final retraining isn't needed since the training at each split will continue from where the previous one left off. Can scikit learn behave like this?
In the neural networks section we tell people to do a 4 way cross validation and once those are complete to retrain on the entire dataset. Is this really the right way to do things? In other neural net frameworks (e.g. keras) you can do multiple iterations of training, so that final retraining isn't needed since the training at each split will continue from where the previous one left off. Can scikit learn behave like this?